Section REF  shows that CycleNet has a higher accuracy than the baseline, but does it learn a substantially different representation of the images?
At the end of a cycle, each element of the output tensor may depend on all elements of the input tensor (see section REF ), thus CycleNet is expected to have large receptive fields.
In contrast, the receptive fields of CNNs are usually small [1]}.
Here, we compute the receptive fields at the output of each cycle of the deepest models of Section REF  (18 layers for CIFAR and 15 layers for ImageNet): first, we compute saliency maps following [2]}, then we use those maps to compute receptive fields [1]}.
